Detection of Depression-Related Posts in Reddit Social Media Forum

By Michael M. Tadesse, Hongfei Lin, Bo Xu, Liang Yang. IEEE Access, 2019

395 citations recorded by the source corpus.

Depression is viewed as the largest contributor to global disability and a major reason for suicide. It has an impact on the language usage reflected in the written text. The key objective of our study is to examine Reddit users’ posts to detect any factors that may reveal the depression attitudes of relevant online users. For such purpose, we employ the Natural Language Processing (NLP) techniques and machine learning approaches to train the data and evaluate the efficiency of our proposed method. We identify a lexicon of terms that are more common among depressed accounts. The results show that our proposed method can significantly improve performance accuracy. The best single feature is bigram with the Support Vector Machine (SVM) classifier to detect depression with 80% accuracy and 0.80 F1 scores. The strength and effectiveness of the combined features (LIWC+LDA+bigram) are most successfully demonstrated with the Multilayer Perceptron (MLP) classifier resulting in the top performance for depression detection reaching 91% accuracy and 0.93 F1 scores. According to our study, better performance improvement can be achieved by proper feature selections and their multiple feature combinations.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/88ea9230d23c77fdca3379170f553db7d763bb50

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Social Psychology

Citations in the corpus

MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2021, background): “Social content from users who wrote about mental health issues and posted suicidal ideation has been widely used to study mental health issues (Ji et al., 2018; Tadesse et al., 2019).”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “However, most of these studies have focused on building domain-specific machine learning (ML) models ( i.e. , one model for one particular task, such as stress detection [35, 58], depression prediction [30, 79, 90], or suicide risk assessment [20, 27]).”
AI in mental health. (2020, background): “Recent work continues research that started to appear after the advent of social media on analysing posted content for signs of mental health issues, with most of this research focusing on depression [24–28 ,29 ].”
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus (2021, neutral): “Delimit, on the other hand, is A BERT-based model trained on Twitter and Reddit posts, not relying on any handcrafted features.”
The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, background): “…al., 2020; Zheng et al., 2020; Zirikly et al., 2019) and depression (e.g., De Choudhury et al., 2013; Eichstaedt et al., 2018; Guntuku et al., 2017; Tadesse et al., 2019), which, in many cases, precedes the actual suicidal behavior (American Psychiatric Association, 2013; Hawton & van Heeringen,…”